Article 4 — Transformers and the rise of modern AI

🧠 Recap

In Article 3, we explored Neural Networks — the digital brains behind deep learning. Now we step into the architecture that changed everything: Transformers — the foundation of ChatGPT, BERT, and nearly all state-of-the-art AI today.

⚡ What Are Transformers?

Transformers are a type of neural network designed to handle sequential data (like text or audio) but in a completely new way — by looking at all parts of the input at once rather than step-by-step.

They introduced a key concept called self-attention, which allows the model to weigh the importance of different words or tokens in a sentence, regardless of their position.

"Transformers don't just read left to right — they see the whole picture."

🧱 Key Components of a Transformer

Let’s break down the building blocks of a Transformer model:

1. Token Embedding

• Converts words or symbols into vectors (numerical form the model can understand).

• Position encoding is added so the model knows the order of tokens.
  
  

2. Multi-Head Self-Attention

• Allows the model to focus on relevant parts of the input.

• Example: In the sentence “The cat sat on the mat because it was warm,” — “it” refers to “mat” — self-attention helps the model figure that out.
  
  

3. Feedforward Network

• Processes each token independently using dense layers to learn deeper patterns.
  
  

4. Layer Normalization & Residual Connections

• These help with stable and efficient training.
  
  

🔧 Transformer Block

A Transformer consists of many identical blocks — each containing:

• Multi-head attention

• Feedforward layers

• Normalization and skip connections

🔄 Data Flow in a Transformer (Simplified)

1. Input Text → Tokenized + Embedded

2. Positional Info Added

3. Goes Through Multiple Transformer Blocks

4. Contextualized Output Vectors

5. Prediction Made (e.g., next word, class label, translation)

🚀 Why Transformers Changed the Game

• No recurrence — they process entire sequences in parallel, making training faster.

• Scalable — can be trained on huge datasets.

• Transferable — pre-trained models can be fine-tuned on specific tasks.

🤯 Real Examples

🧠 How Are Transformers Trained?

1. Huge datasets are used (e.g., the entire internet).

2. Models learn using self-supervised learning — predicting masked words or next tokens.

3. They use gradient descent and backpropagation to adjust billions of weights.

We’ll cover the learning process and weights in more depth in Article 5.

📚 Coming Up in Article 5

Next, we’ll look inside the learning process: how models train, how weights and embeddings are formed, and what happens when you send a prompt to an AI.

🔑 Key Takeaway

Transformers revolutionized AI by enabling models to understand context better than ever. They power today’s smartest tools — and they’re still evolving.